Advanced Deep Learning Techniques for Accurate Agricultural Price Predictions in India

Saturday, 27 July 2024, 00:29

Accurate price forecasting of agricultural commodities is vital for India's economy. Traditional and machine learning models fall short, but recent advancements in deep learning have shown promise in addressing these challenges. This study evaluates deep learning models such as NBEATSX and TransformerX against traditional methods and machine learning algorithms, emphasizing the importance of exogenous variables in improving forecasting accuracy. The findings demonstrate that deep learning methods significantly outperform traditional models, bridging a critical gap in agricultural data analysis and offering a path for enhanced price prediction strategies.

Nature — Advanced Deep Learning Techniques for Accurate Agricultural Price Predictions in India

Introduction

Accurately predicting agricultural commodity prices is crucial for India's economy. Traditional parametric models struggle with stringent assumptions, while machine learning (ML) approaches, though data-driven, lack automatic feature extraction.

Deep Learning Models

Deep learning (DL) models, with advanced feature extraction and predictive abilities, offer a promising solution. This study explored advanced versions of the well-known univariate models, NBEATSX and TransformerX.

Research Methodology

Utilized price data for essential crops like Tomato, Onion, and Potato (TOP) from major Indian markets.
Complemented with corresponding weather data (precipitation and temperature).
Compared performance with traditional statistical methods (ARIMAX and MLR) and various ML algorithms (ANN, SVR, RFR, and XGBoost).

Performance Evaluation

The performance was rigorously evaluated using error metrics including:

RMSE
MAE
sMAPE
MASE
QL

Findings

The findings indicate that DL models, particularly when augmented with exogenous variables, consistently outshone other methods. NBEATSX and TransformerX showcased average RMSE of 110.33 and 135.33, respectively. They demonstrated lower error metrics compared to both statistical and ML models, underscoring their effectiveness and potential in agricultural crop price forecasting.

Conclusion

This study bridged a crucial research gap and highlighted the robust potential of DL models in enhancing the accuracy of agricultural commodity price predictions in India.

This article was prepared using information from open sources in accordance with the principles of Ethical Policy. The editorial team is not responsible for absolute accuracy, as it relies on data from the sources referenced.